Senior Machine Learning Engineer
New
C
CI&TData & AI
BrazilFull-TimeSenior
Salary not disclosed
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Job Details
- Languages
- Intermediate English
- Required Skills
- PythonSQLMLFlowCI/CDDatabricksAzure DevOpsMLOpsPySpark
Requirements
- Advanced experience with Databricks, including MLflow, Unity Catalog, Delta Lake, Databricks Workflows, Model Registry, Model Serving, and Databricks Asset Bundles (DABs).
- Strong experience developing, operationalizing, and monitoring Machine Learning models in production.
- Experience with Feature Engineering, hyperparameter optimization, model evaluation, and supervised and unsupervised learning algorithms.
- Experience with enterprise Feature Stores, including feature versioning and point-in-time lookups.
- Knowledge of Data Drift, Concept Drift, Performance Drift, and observability of data and ML pipelines.
- Experience building CI/CD pipelines, managing DEV, QA, and PROD environments, and implementing Infrastructure as Code.
- Experience with automated testing for data and Machine Learning pipelines.
- Experience with distributed processing and Spark workload optimization.
- Strong proficiency in Python, PySpark, SQL, MLflow, Spark MLlib, and key Machine Learning ecosystem libraries.
- Knowledge of secure credential and secrets management, such as Service Principals, Key Vault, or equivalent solutions.
- Experience with Azure DevOps or equivalent tools.
- Intermediate English proficiency with the ability to interact with global teams and produce technical documentation.
Responsibilities
- Lead MLOps initiatives, including model training, deployment, model serving, monitoring, and lifecycle governance.
- Develop and maintain ETL/ELT pipelines, DAGs, and data and Machine Learning workflows using PySpark.
- Design and manage enterprise Feature Stores, ensuring feature versioning, lineage, and consistency between training and inference.
- Develop, validate, and operationalize Machine Learning models for different analytical use cases.
- Implement model versioning strategies, Champion/Challenger approaches, rollouts, model promotion, and Model Registry management.
- Ensure observability, quality, traceability, reproducibility, and governance across data, features, pipelines, and models.
- Design and implement CI/CD processes and Infrastructure as Code (IaC) for Machine Learning platforms.
- Define architectural standards, engineering best practices, and MLOps guidelines.
- Conduct technical code reviews, support Data Scientists in industrializing ML solutions, and maintain technical, architectural, and operational documentation.
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